Estradiol regulates human QT-interval: acceleration of cardiac repolarization by enhanced KCNH2 membrane trafficking.

BACKGROUND: Modulation of cardiac repolarization by sexual hormones is controversial and hormonal effects on ion channels remain largely unknown. In the present translational study, we therefore assessed the relationship between QTc duration and gonadal hormones and studied underlying mechanisms. METHODS AND RESULTS: We measured hormone levels and QTc intervals in women during clomiphene stimulation for infertility and women before, during, and after pregnancy. Three heterozygous LQT-2 patients (KCNH2-p.Arg752Pro missense mutation) and two unaffected family members additionally were studied during their menstrual cycles. A comprehensive cellular and molecular analysis was done to identify the mechanisms of hormonal QT-interval regulation. High estradiol levels, but neither progesterone nor estradiol/progesterone ratio, inversely correlated with QTc. Consistent with clinical data, in vitro estradiol stimulation (60 pmol/L, 48 h) enhanced IKCNH2. This increase was mediated by estradiol receptor-α-dependent promotion of KCNH2-channel trafficking to the cell membrane. To study the underlying mechanism, we focused on heat-shock proteins. The heat-shock protein-90 (Hsp90) inhibitor geldanamycin abolished estradiol-induced increase in IKCNH2. Geldanamycin had no effect on KCNH2 transcription or translation; nor did it affect expression of estradiol receptors and chaperones. Estradiol enhanced the physical interaction of KCNH2-channel subunits with heat-shock proteins and augmented ion-channel trafficking to the membrane. CONCLUSION: Elevated estradiol levels were associated with shorter QTc intervals in healthy women and female LQT-2 patients. Estradiol acts on KCNH2 channels via enhanced estradiol-receptor-α-mediated Hsp90 interaction, augments membrane trafficking and thereby increases repolarizing current. These results provide mechanistic insights into hormonal control of human ventricular repolarization and open novel therapeutic avenues.

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation.

Automated Electrocardiogram (ECG) classification using deep neural networks requires large datasets annotated by medical professionals, which is time-consuming and expensive. This work examines ECG augmentation as a method for enriching existing datasets at low cost. First, we introduce three novel augmentations: Limb Electrode Move and Chest Electrode Move both simulate a minor electrode mislocation during signal measurement, and Heart Vector Transform generates an ECG by modeling a rotated main heart axis. These techniques are then combined with nine time series signal augmentations from literature. Evaluation was performed on ICBEB, PTB-XL Diagnostic, PTB-XL Rhythm, and PTB-XL Form datasets. Compared to models trained without data augmentation, area under the receiver operating characteristic curve (AUC) was increased by 3.5%, 1.7%, 1.4% and 3.5%, respectively. Our experiments demonstrated that data augmentation can improve deep learning performance in ECG classification. Analyses of the individual augmentation effects established the efficacy of the three proposed augmentations.

Biventricular pacing does not affect microvolt T-wave alternans in heart failure patients.

BACKGROUND: Microvolt T-wave alternans (MTWA) is a valuable tool for stratification of patients at risk for sudden death and has recently been approved for this purpose by Medicare. Although right atrial (RA) pacing has been applied for MTWA testing, the effects of other pacing modalities on MTWA have not been systematically studied. Accordingly, it is unknown whether biventricular (BiV) pacing might influence MTWA test results. OBJECTIVE: This study sought to investigate effects of BiV pacing in comparison with other pacing modalities. METHODS: Congestive heart failure patients (n = 30) receiving cardiac resynchronization therapy were included, and a systematic step-up pacing protocol was performed via the implanted cardioverter-defibrillator. RESULTS: Of the overall 120 MTWA tests performed, 67 (56%) were nonnegative. Nonnegative MTWA test results were observed in 18 patients (60%) during RA stimulation, whereas 17 (57%), 15 (50%), and 17 test results (57%) were nonnegative during right ventricular (RV), left ventricular (LV), and BiV pacing, respectively. Seven (23%) patients were MTWA negative for all pacing sites. Results of MTWA assessment during RA pacing were concordant with results obtained with RV pacing in 25 (83%) patients (kappa = 0.66, P = .0003), to LV pacing in 21 (70%) patients (kappa = 0.4, P = .025), and to BiV pacing in 25 (83%) patients (kappa = 0.66, P = .0003). Positive and negative predictive values of nonnegative MTWA test results obtained during RA pacing for a similar result obtained with RV pacing were 88% and 76%. Respective values were similar for other pacing modalities (80% and 60% for LV; 88% and 76% for BiV pacing). CONCLUSION: There is a high level of concordance between MTWA test results obtained during RA pacing and other pacing modalities, and MTWA assessment seems not to be influenced by BiV stimulation in congestive heart failure patients. In general, BiV pacing does not seem to affect an arrhythmogenic substrate as detected by MTWA testing.

Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.

Cellular properties of C-terminal KCNH2 long QT syndrome mutations: description and divergence from clinical phenotypes.

BACKGROUND: C-terminal KCNH2 mutations are commonly associated with a more benign clinical presentation, but mutations localized in close proximity may exhibit different clinical and biophysical phenotypes. The value of detailed cellular characterization of such mutant channels in vitro has not been studied with respect to clinical risk stratification of affected patients. OBJECTIVE: The purpose of this study was to study the cellular properties and clinical presentation of C-terminal KCNH2 missense mutations localized in close proximity. METHODS: Unrelated female index patients with KCNH2 mutations and heterogeneous clinical presentation were identified. Mutations were studied in vitro with biophysical and molecular biology techniques. RESULTS: Ionic currents from all three mutants were reduced compared with wild type. Coexpression experiments mimicking heterozygosity indicated haploinsufficiency as the mechanism of current suppression in all cases. One mutation (R954C) was associated with reversible QTc prolongation during macrolide treatment (QTc approximately 600 ms). Biophysical properties included reduced current amplitude, accelerated deactivation, and altered activation voltage dependence. The patient affected by L955V suffered from recurrent syncope (QTc approximately 460 ms), and this mutation led to greatly reduced current and reduced KCNH2 protein in plasma membrane preparations. Confocal microscopy supported these findings, suggesting aggregate formation and endoplasmic reticulum retention by L955V. The mutation carrier of G1036D (QTc approximately 530 ms) was resuscitated from cardiac arrest, but biophysical characteristics were less strongly affected. CONCLUSION: The results of our study provide evidence that C-terminal mutations localized in proximity to each other may exhibit strongly different and poorly correlated clinical and cellular phenotypes. These findings provide evidence that even detailed characterization of long QT syndrome mutations may not provide additional definitive information for clinical risk stratification.